The accumulation of genomic information and technology is opening doors for the discovery of new diagnostics, preventive strategies, and drug therapies for a whole host of diseases, including diabetes, hypertension, heart disease, cancer, and mental illness. This is due to the fact that many human diseases have genetic components, which may be evidenced by clustering in certain families, and/or in certain racial, ethnic or ethnogeographic (world population) groups. For example, prostrate cancer clusters in some families. Furthermore, while prostate cancer is common among all U.S. males, it is especially common among African American men. They are 35 percent more likely than Americans of European descent to develop the disease and more than twice as likely to die from it. A variation on chromosome 1 (HPC1) and a variation on the X chromosome (HPCX) appear to predispose men to prostrate cancer and a study is currently underway to test this hypothesis.
Likewise, it is clear that an individual's genes can have considerable influence over how that individual responds to a particular drug or drugs.
Individuals inherit specific versions of enzymes that affect how they metabolize, absorb and excrete drugs. So far, researchers have identified several dozen enzymes that vary in their activity throughout the population and that probably dictate people's response to drugs—which may be good, bad or sometimes deadly. For example, the cytochrome P450 family of enzymes (of which CYP 2D6 is a member) is involved in the metabolism of at least 20 percent of all commonly prescribed drugs, including the antidepressant Prozac™, the painkiller codeine, and high-blood-pressure medications such as captopril. Ethnic variation is also seen in this instance. Due to genetic differences in cytochrome P450, for example, 6 to 10 percent of Whites, 5 percent of Blacks, and less than 1 percent of Asians are poor drug metabolizers.
One very troubling observation is that adverse reactions often occur in patients receiving a standard dose of a particular drug. As an example, doctors in the 1950s would administer a drug called succinylcholine to induce muscle relaxation in patients before surgery. A number of patients, however, never woke up from anesthesia—the compound paralyzed their breathing muscles and they suffocated. It was later discovered that the patients who died had inherited a mutant form of the enzyme that clears succinylcholine from their system. As another example, as early as the 1940s doctors noticed that certain tuberculosis patients treated with the antibacterial drug isoniazid would feel pain, tingling and weakness in their limbs. These patients were unusually slow to clear the drug from their bodies—isoniazid must be rapidly converted to a nontoxic form by an enzyme called N-acetyltransferase. This difference in drug response was later discovered to be due to differences in the gene encoding the enzyme. The number of people who would experience adverse responses using this drug is not small. Forty to sixty percent of Caucasians have the less active form of the enzyme (i.e., “slow acetylators”).
Another gene encodes a liver enzyme that causes side effects in some patients who used Seldane™, an allergy drug which was removed from the market. The drug Seldane™ is dangerous to people with liver disease, on antibiotics, or who are using the antifungal drug Nizoral. The major problem with Seldane™ is that it can cause serious, potentially fatal, heart rhythm disturbances when more than the recommended dose is taken. The real danger is that it can interact with certain other drugs to cause this problem at usual doses. It was discovered that people with a particular version of a CYP450 suffered serious side effects when they took Seldane™ with the antibiotic erythromycin.
Sometimes one ethnic group is affected more than others. During the Second World War, for example, African-American soldiers given the antimalarial drug primaquine developed a severe form of anaemia. The soldiers who became ill had a deficiency in an enzyme called glucose-6-phosphate dehydrogenase (G6PD) due to a genetic variation that occurs in about 10 percent of Africans, but very rarely in Caucasians. G6PD deficiency probably became more common in Africans because it confers some protection against malaria.
Variations in certain genes can also determine whether a drug treats a disease effectively. For example, a cholesterol-lowering drug called pravastatin won't help people with high blood cholesterol if they have a common gene variant for an enzyme called cholesteryl ester transfer protein (CETP). As another example, several studies suggest that the version of the “ApoE” gene that is associated with a high risk of developing Alzheimer's disease in old age (i.e., APOE4) correlates with a poor response to an Alzheimer's drug called tacrine. As yet another example, the drug Herceptin™, a treatment for metastatic breast cancer, only works for patients whose tumors overproduce a certain protein, called HER2. A screening test is given to all potential patients to weed out those on whom the drug won't be effective.
In summary, it is well known that not all individuals respond identically to drugs for a given condition. Some people respond well to drug A but poorly to drug B, some people respond better to drug B, while some have adverse reactions to both drugs. In many cases it is currently difficult to tell how an individual person will respond to a given drug, except by having them try using it.
It appears that a major reason people respond differently to a drug is that they have different forms of one or more of the proteins that interact with the drug or that lie in the cascade initiated by taking the drug.
A common method for determining the genetic differences between individuals is to find Single Nucleotide Polymorphisms (SNPs), which may be either in or near a gene on the chromosome, that differ between at least some individuals in the population. A number of instances are known (Sickle Cell Anemia is a prototypical example) for which the nucleotide at a SNP is correlated with an individual's propensity to develop a disease. Often these SNPs are linked to the causative gene, but are not themselves causative. These are often called surrogate markers for the disease. The SNP/surrogate marker approach suffers from at least three problems:
(1) Comprehensiveness: There are often several polymorphisms in any given gene. (See Ref. 10 for an example in which there are 88 polymorphic sites). Most SNP projects look at a large number of SNPs, but spread over an enormous region of the chromosome. Therefore the probability of finding all (or any) SNPs in the coding region of a gene is small. The likelihood of finding the causative SNP(s) (the subset of polymorphisms responsible for causing a particular condition or change in response to a treatment) is even lower.
(2) Lack of Linkage: If the causative SNP is in so-called linkage disequilibrium (Ref 1, Chapter 2) with the measured SNP, then the nucleotide at the measured SNP will be correlated with the nucleotide at the causative SNP. However it is impossible to predict a priori whether such linkage disequilibrium will exist for a particular pair of measured and causative SNPs.
(3) Phasing: When there are multiple, interacting causative SNPs in a gene one needs to know what are the sequences of the two forms of the gene present in an individual. For instance, assume there is a gene that has 3 causative SNPs and that the remaining part of the gene is identical among all individuals. We can then identify the two copies of the gene that any individual has with only the nucleotides at those sites. Now assume that 4 forms exist in the population, labeled TAA, ATA, TTA and AAA. SNP methods effectively measure SNPs one at a time, and leave the “phasing” between nucleotides at different positions ambiguous. An individual with one copy of TAA and one of ATA would have a genotype (collection of SNPs) of [T/A, T/A, A/A]. This genotype is consistent with the haplotypes TTA/AAA or TAA/ATA. An individual with one copy of TTA and one of AAA would have exactly the same genotype as an individual with one copy of TAA and one copy of ATA. By using unphased genotypes, we cannot distinguish these two individuals.
A relatively low density SNP based map of the genome will have little likelihood of specifically identifying drug target variations that will allow for distinguishing responders from poor responders, non-responders, or those likely to suffer side-effects (or toxicity) to drugs. A relatively low density SNP based map of the genome also will have little likelihood of providing information for new genetically based drug design. In contrast, using the data and analytical tools of the present invention, knowing all the polymorphisms in the haplotypes will provide a firm basis for pursuing pharmacogenetics of a drug or class of drugs.
With the present invention, by knowing which forms of the proteins an individual possesses, in particular, by knowing that individual's haplotypes (which are the most detailed description of their genetic makeup for the genes of interest) for rationally chosen drug target genes, or genes intimately involved with the pathway of interest, and by knowing the typical response for people with those haplotypes, one can with confidence predict how that individual will respond to a drug. Doing this has the practical benefit that the best available drug and/or dose for a patient can be prescribed immediately rather than relying on a trial and error approach to find the optimal drug. The end result is a reduction in cost to the health care system. Repeat visits to the physician's office are reduced, the prescription of needless drugs is avoided, and the number of adverse reactions is decreased.
The Clinical Trials Solution (CTS™) method described herein provides a process for finding correlation's between haplotypes and response to treatment and for developing protocols to test patients and predict their response to a particular treatment.
The CTS™ method is partially embodied in the DecoGen™ Platform, which is a computer program coupled to a database used to display and analyze genetic and clinical information. It includes novel graphical and computational methods for treating haplotypes, genotypes, and clinical data in a consistent and easy-to-interpret manner.